🎁 New Features

It's been a colorful month ... we've added the ability to change the color of any categorical variable and extended the spectrum of colors automatically generated for your values. We've also added a new enrichment letting you fill missing values in your data!

01. Extended Variable Colors

We've increased the scale of our default color palette to include 30 colors! 

On top of this - clicking to show more categorical values will add appropriate color to nodes in your Graph that would previously have been grey.

Color is a powerful analytical tool and lets you quickly identify the features of your data points inside visualisations. Up until now, we've used grey to color any categorical value beyond the 10 most frequently occurring. 

We believe that more color means more clarity. Clicking to see more categorical values will extend the colors presented in your Graph. 

How can I start using it?

• Open a project with a categorical variable that contains many values.
• Apply color mapping to this variable in your project's Graph.
• Click to show more values.
• Notice how your color palette extends inside the Graph and variable sidebar chart.

02. Changing Colors For Any Categorical Value

You can now change the color of any categorical value!

Although every value in your categorical variable will be automatically assigned a color - you can change these by editing the variable and selecting a new one using the color picker. 

How can I start using it?

• Open up a project and find a categorical variable. 
• Click the edit button at the top of your variable card. 
• Now, click the color picker icon. 
• Choose a new color and save your changes!

03. New Enrichment: Fill Missing Values 

We've built an enrichment to fill missing values in your data. Missing values can be annoying, misleading and disruptive to your analysis. Replacing them with specific values can help to clean up and prepare your dataset for analysis.

Choose Fill Missing Values from the data enrichment tab inside of the project setup wizard to start replacing missing values. Then, select a variable with missing values and tell Graphext how you would like to fill these values. You can choose from options like using a constant value, using the most or least frequently occurring value and using the column's minimum or maximum value. Look for the replaced variable in your transformed dataset.

If you'd prefer, you can always use a different enrichment to predict missing values!

How can I start using it?

• Start building a project using a dataset with missing values.
• Choose Fill Missing Values from the data enrichment tab inside your project setup wizard.
• Tell Graphext which column contains your missing values.
• Choose how you want to replace your missing values.
• That's it. Look for the replaced variable in your transformed dataset.

04: Dataset Info: Sources & Descriptions

We've added space to describe your dataset and reference it's original source inside Graphext. 

Context is always important but when dealing with data - it is essential. Referencing your data leaves behind a trail that other team members or researchers can trace to validate or continue your analysis. 

To start describing and referencing a dataset, find it from inside of your team's Graphext workspace. Then open the dataset info menu using the 3 dots on the far right of your dataset card. Enter the source URL and write a description then click on the dataset to see this information listed above your data. 

How Can I Start Using It?

• Find a dataset inside your Graphext workspace and select the three dots from the far right of the dataset card.
• Choose Dataset Info from the menu list.
• Enter a source URL and write a short description.
• Save your changes.

🐞 Bug Fixes & Improvements

• Added the ability to change the name of a team.
• Fixed an issue with info cards not appearing after clicking on a node in the Graph.
• Fixed a bug causing the creation of a new project to fail after moving some data to the trash.
• Added a menu button to instantly open a variable in the Correlations panel.
• Added a legend to list variables. A white circle indicates that white-coloured nodes refer to data belonging to more than one list category.

📖 Stories worth Sharing

01. Segmenting 1000 Supermarket Customers Using Sales Data 

Our team clustered 1000 supermarket sales in order to segment customers according to their buying habits.

Graphext | Graphtex | Graphnext: Grouping Similar Spellings Using Chars2Vec and Agglomerative Clustering

'España' and 'Españha' are just spelling variations. We built a way of grouping words spelt differently but referring to the same concept and made it available alongside any type of analysis you perform with Graphext. 

Getting Started Videos

We've created a collection of Getting Started videos to help guide you in using Graphext's interface panels and core features.